Q1: What is the trend in cases, mortality across geopgraphical regions?
Plot # of cases vs time
* For each geographical set:
* comparative longitudinal case trend (absolute & log scale)
* comparative longitudinal mortality trend
* death vs total correlation
| comparative_longitudinal_case_trend |
long |
time |
log_cases |
geography |
none (case type?) |
case_type |
[15, 50, 4] geography x (2 scale?) case type |
| comparative longitudinal case trend |
long |
time |
cases |
geography |
case_type |
? |
[15, 50, 4] geography x (2+ scale) case type |
| comparative longitudinal mortality trend |
wide |
time |
mortality rate |
geography |
none |
none |
[15, 50, 4] geography |
| death vs total correlation |
wide |
cases |
deaths |
geography |
none |
none |
[15, 50, 4] geography |
# total cases vs time
# death cases vs time
# mortality rate vs time
# death vs mortality
# death vs mortality
# total & death case vs time (same plot)
#<question> <x> <y> <colored> <facet> <dataset>
## trend in case/deaths over time, comapred across regions <time> <log cases> <geography*> <none> <.wide>
## trend in case/deaths over time, comapred across regions <time> <cases> <geography*> <case_type> <.long>
## trend in mortality rate over time, comapred across regions <time> <mortality rate> <geography*> <none>
## how are death/mortality related/correlated? <time> <log cases> <geography*> <none>
## how are death and case load correlated? <cases> <deaths>
# lm for each?? - > apply lm from each region starting from 100th case. m, b associated with each.
# input: geographical regsion, logcase vs day (100th case)
# output: m, b for each geographical region ID
#total/death on same plot- diffeer by 2 logs, so when plotting log, use pch. when plotting absolute, need to use free scales
#when plotting death and case on same, melt.
#CoronaCases - > filter sets (3)
#world - choose countries with sufficent data
N<-ddply(filter(Corona_Cases,Total_confirmed_cases>100),c("Country.Region"),summarise,n=length(Country.Region))
ggplot(filter(N,n<100),aes(x=n))+
geom_histogram()+
default_theme+
ggtitle("Distribution of number of days with at least 100 confirmed cases for each region")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

kable(arrange(N,-n),caption="Sorted number of days with at least 100 confirmed cases")
Sorted number of days with at least 100 confirmed cases
| US_state |
38588 |
| China |
126 |
| Diamond Princess |
107 |
| Korea, South |
97 |
| Japan |
96 |
| Italy |
94 |
| Iran |
91 |
| Singapore |
88 |
| France |
87 |
| Germany |
87 |
| Spain |
86 |
| US |
85 |
| Switzerland |
83 |
| United Kingdom |
83 |
| Belgium |
82 |
| Netherlands |
82 |
| Norway |
82 |
| Sweden |
82 |
| Austria |
80 |
| Malaysia |
79 |
| Australia |
78 |
| Bahrain |
78 |
| Denmark |
78 |
| Canada |
77 |
| Qatar |
77 |
| Iceland |
76 |
| Brazil |
75 |
| Czechia |
75 |
| Finland |
75 |
| Greece |
75 |
| Iraq |
75 |
| Israel |
75 |
| Portugal |
75 |
| Slovenia |
75 |
| Egypt |
74 |
| Estonia |
74 |
| India |
74 |
| Ireland |
74 |
| Kuwait |
74 |
| Philippines |
74 |
| Poland |
74 |
| Romania |
74 |
| Saudi Arabia |
74 |
| Indonesia |
73 |
| Lebanon |
73 |
| San Marino |
73 |
| Thailand |
73 |
| Chile |
72 |
| Pakistan |
72 |
| Luxembourg |
71 |
| Peru |
71 |
| Russia |
71 |
| Ecuador |
70 |
| Mexico |
70 |
| Slovakia |
70 |
| South Africa |
70 |
| United Arab Emirates |
70 |
| Armenia |
69 |
| Colombia |
69 |
| Croatia |
69 |
| Panama |
69 |
| Serbia |
69 |
| Taiwan* |
69 |
| Turkey |
69 |
| Argentina |
68 |
| Bulgaria |
68 |
| Latvia |
68 |
| Uruguay |
68 |
| Algeria |
67 |
| Costa Rica |
67 |
| Dominican Republic |
67 |
| Hungary |
67 |
| Andorra |
66 |
| Bosnia and Herzegovina |
66 |
| Jordan |
66 |
| Lithuania |
66 |
| Morocco |
66 |
| New Zealand |
66 |
| North Macedonia |
66 |
| Vietnam |
66 |
| Albania |
65 |
| Cyprus |
65 |
| Malta |
65 |
| Moldova |
65 |
| Brunei |
64 |
| Burkina Faso |
64 |
| Sri Lanka |
64 |
| Tunisia |
64 |
| Ukraine |
63 |
| Azerbaijan |
62 |
| Ghana |
62 |
| Kazakhstan |
62 |
| Oman |
62 |
| Senegal |
62 |
| Venezuela |
62 |
| Afghanistan |
61 |
| Cote d’Ivoire |
61 |
| Cuba |
60 |
| Mauritius |
60 |
| Uzbekistan |
60 |
| Cambodia |
59 |
| Cameroon |
59 |
| Honduras |
59 |
| Nigeria |
59 |
| West Bank and Gaza |
59 |
| Belarus |
58 |
| Georgia |
58 |
| Bolivia |
57 |
| Kosovo |
57 |
| Kyrgyzstan |
57 |
| Montenegro |
57 |
| Congo (Kinshasa) |
56 |
| Kenya |
55 |
| Niger |
54 |
| Guinea |
53 |
| Rwanda |
53 |
| Trinidad and Tobago |
53 |
| Paraguay |
52 |
| Bangladesh |
51 |
| Djibouti |
49 |
| El Salvador |
48 |
| Guatemala |
47 |
| Madagascar |
46 |
| Mali |
45 |
| Congo (Brazzaville) |
42 |
| Jamaica |
42 |
| Gabon |
40 |
| Somalia |
40 |
| Tanzania |
40 |
| Ethiopia |
39 |
| Burma |
38 |
| Sudan |
37 |
| Liberia |
36 |
| Maldives |
34 |
| Equatorial Guinea |
33 |
| Cabo Verde |
31 |
| Sierra Leone |
29 |
| Guinea-Bissau |
28 |
| Togo |
28 |
| Zambia |
27 |
| Eswatini |
26 |
| Chad |
25 |
| Tajikistan |
24 |
| Haiti |
22 |
| Sao Tome and Principe |
22 |
| Benin |
20 |
| Nepal |
20 |
| Uganda |
20 |
| Central African Republic |
19 |
| South Sudan |
19 |
| Guyana |
17 |
| Mozambique |
16 |
| Yemen |
12 |
| Mongolia |
11 |
| Mauritania |
8 |
| Nicaragua |
8 |
| Malawi |
2 |
| Syria |
2 |
# Pick top 15 countries with data
max_colors<-12
# find way to fix this- China has diff provences. Plot doesnt look right...
sufficient_data<-arrange(filter(N,!Country.Region %in% c("US_state", "Diamond Princess")),-n)[1:max_colors,]
kable(sufficient_data,caption = paste0("Top ",max_colors," countries with sufficient data"))
Top 12 countries with sufficient data
| China |
126 |
| Korea, South |
97 |
| Japan |
96 |
| Italy |
94 |
| Iran |
91 |
| Singapore |
88 |
| France |
87 |
| Germany |
87 |
| Spain |
86 |
| US |
85 |
| Switzerland |
83 |
| United Kingdom |
83 |
Corona_Cases.world<-filter(Corona_Cases,Country.Region %in% c(sufficient_data$Country.Region))
#us
# - by state
Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
# summarize
#!City %in% c("Unassigned")
# - specific cities
#mortality_rate!=Inf & mortality_rate<=1
Corona_Cases.UScity<-filter(Corona_Cases,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") & City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May"))
measure_vars_long<-c("Total_confirmed_cases.log","Total_confirmed_cases","Total_confirmed_deaths","Total_confirmed_deaths.log")
melt_arg_list<-list(variable.name = "case_type",value.name = "cases",measure.vars = c("Total_confirmed_cases","Total_confirmed_deaths"))
melt_arg_list$data=NULL
melt_arg_list$data=select(Corona_Cases.world,-ends_with(match = "log"))
Corona_Cases.world.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.UScity,-ends_with(match = "log"))
Corona_Cases.UScity.long<-do.call(melt,melt_arg_list)
melt_arg_list$data=select(Corona_Cases.US_state,-ends_with(match = "log"))
Corona_Cases.US_state.long<-do.call(melt,melt_arg_list)
Corona_Cases.world.long$cases.log<-log(Corona_Cases.world.long$cases,10)
Corona_Cases.US_state.long$cases.log<-log(Corona_Cases.US_state.long$cases,10)
Corona_Cases.UScity.long$cases.log<-log(Corona_Cases.UScity.long$cases,10)
# what is the current death and total case load for US? For world? For states?
#-absolute
#-log
# what is mortality rate (US, world)
#-absolute
#how is death and case correlated? (US, world)
#-absolute
#Corona_Cases.US<-filter(Corona_Cases,Country.Region=="US" & Total_confirmed_cases>0)
#Corona_Cases.US.case100<-filter(Corona_Cases.US, Days_since_100>=0)
# linear model parameters
#(model_fit<-lm(formula = Total_confirmed_cases.log~Days_since_100,data= Corona_Cases.US.case100 ))
#(slope<-model_fit$coefficients[2])
#(intercept<-model_fit$coefficients[1])
# Correlation coefficient
#cor(x = Corona_Cases.US.case100$Days_since_100,y = Corona_Cases.US.case100$Total_confirmed_cases.log)
##------------------------------------------
## Plot World Data
##------------------------------------------
# Timestamp for world
timestamp_plot.world<-paste("Most recent date for which data available:",max(Corona_Cases.world$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")
# Base template for plots
baseplot.world<-ggplot(data=NULL,aes(x=Days_since_100,col=Country.Region))+
default_theme+
scale_color_brewer(type = "qualitative",palette = "Paired")+
ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
theme(legend.position = "bottom",plot.title = element_text(size=12))
##/////////////////////////
### Plot Longitudinal cases
(Corona_Cases.world.long.plot<-baseplot.world+
geom_point(data=Corona_Cases.world.long,aes(y=cases))+
geom_line(data=Corona_Cases.world.long,aes(y=cases))+
facet_wrap(~case_type,scales = "free_y",ncol=1)+
ggtitle(timestamp_plot.world)
)

(Corona_Cases.world.loglong.plot<-baseplot.world+
geom_point(data=Corona_Cases.world.long,aes(y=cases.log))+
geom_line(data=Corona_Cases.world.long,aes(y=cases.log))+
facet_wrap(~case_type,scales = "free_y",ncol=1)+
ggtitle(timestamp_plot.world))

##/////////////////////////
### Plot Longitudinal mortality rate
(Corona_Cases.world.mortality.plot<-baseplot.world+
geom_point(data=Corona_Cases.world,aes(y=mortality_rate))+
geom_line(data=Corona_Cases.world,aes(y=mortality_rate))+
ylim(c(0,0.3))+
ggtitle(timestamp_plot.world))
## Warning: Removed 100 rows containing missing values (geom_point).
## Warning: Removed 100 row(s) containing missing values (geom_path).

##/////////////////////////
### Plot death vs total case correlation
(Corona_Cases.world.casecor.plot<-ggplot(Corona_Cases.world,aes(x=Total_confirmed_cases,y=Total_confirmed_deaths,col=Country.Region))+
geom_point()+
geom_line()+
default_theme+
scale_color_brewer(type = "qualitative",palette = "Paired")+
ggtitle(paste("Log10 cases over time,",timestamp_plot.world))+
theme(legend.position = "bottom",plot.title = element_text(size=12))+
ggtitle(timestamp_plot.world))

### Write polots
write_plot(Corona_Cases.world.long.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.long.plot.png"
write_plot(Corona_Cases.world.loglong.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.loglong.plot.png"
write_plot(Corona_Cases.world.mortality.plot,wd = results_dir)
## Warning: Removed 100 rows containing missing values (geom_point).
## Warning: Removed 100 row(s) containing missing values (geom_path).
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.mortality.plot.png"
write_plot(Corona_Cases.world.casecor.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/Corona_Cases.world.casecor.plot.png"
##------------------------------------------
## Plot US State Data
##-----------------------------------------
baseplot.US<-ggplot(data=NULL,aes(x=Days_since_100_state,col=case_type))+
default_theme+
facet_wrap(~Province.State)+
ggtitle(paste("Log10 cases over time,",timestamp_plot.world))
Corona_Cases.US_state.long.plot<-baseplot.US+geom_point(data=Corona_Cases.US_state.long,aes(y=cases.log))
##------------------------------------------
## Plot US City Data
##-----------------------------------------
Corona_Cases.US.plotdata<-filter(Corona_Cases.US_state,Province.State %in% c("Pennsylvania","Maryland","New York","New Jersey") &
City %in% c("Bucks","Baltimore City", "New York","Burlington","Cape May") &
Total_confirmed_cases>0)
timestamp_plot<-paste("Most recent date for which data available:",max(Corona_Cases.US.plotdata$Date))#timestamp(quiet = T,prefix = "Updated ",suffix = " (EST)")
city_colors<-c("Bucks"='#beaed4',"Baltimore City"='#386cb0', "New York"='#7fc97f',"Burlington"='#fdc086',"Cape May"="#e78ac3")
##/////////////////////////
### Plot death vs total case correlation
(Corona_Cases.city.loglong.plot<-ggplot(melt(Corona_Cases.US.plotdata,measure.vars = c("Total_confirmed_cases.log","Total_confirmed_deaths.log"),variable.name = "case_type",value.name = "cases"),aes(x=Date,y=cases,col=City,pch=case_type))+
geom_point(size=4)+
geom_line()+
default_theme+
#facet_wrap(~case_type)+
ggtitle(paste("Log10 total and death cases over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
scale_color_manual(values = city_colors)+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.long.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases,col=City))+
geom_point(size=4)+
geom_line()+
default_theme+
facet_grid(~Province.State,scales = "free_y")+
ggtitle(paste("MD, PA, NJ total cases over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))
+
scale_color_manual(values = city_colors)+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.mortality.plot<-ggplot(Corona_Cases.US.plotdata,aes(x=Date,y=mortality_rate,col=City))+
geom_point(size=3)+
geom_line(size=2)+
default_theme+
ggtitle(paste("Mortality rate (deaths/total) over time,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
scale_color_manual(values = city_colors)+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

(Corona_Cases.city.casecor.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(y=Total_confirmed_deaths,x=Total_confirmed_cases,col=City))+
geom_point(size=3)+
geom_line(size=2)+
default_theme+
ggtitle(paste("Correlation of death vs total cases,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12))+
scale_color_manual(values = city_colors))

(Corona_Cases.city.long.normalized.plot<-ggplot(filter(Corona_Cases.US.plotdata,Province.State !="New York"),aes(x=Date,y=Total_confirmed_cases.per100,col=City))+
geom_point(size=4)+
geom_line()+
default_theme+
facet_grid(~Province.State)+
ggtitle(paste("MD, PA, NJ total cases over time per 100 people,",timestamp_plot))+
theme(legend.position = "bottom",plot.title = element_text(size=12),axis.text.x = element_text(angle=45,hjust=1))+
scale_color_manual(values = city_colors) +
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))

write_plot(Corona_Cases.city.long.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.plot.png"
write_plot(Corona_Cases.city.loglong.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.loglong.plot.png"
write_plot(Corona_Cases.city.mortality.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.mortality.plot.png"
write_plot(Corona_Cases.city.casecor.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.casecor.plot.png"
write_plot(Corona_Cases.city.long.normalized.plot,wd = results_dir_custom)
## [1] "/Users/stevensmith/Projects/coronavirus/results/custom/Corona_Cases.city.long.normalized.plot.png"
Q1b what is the model
Fit the cases to a linear model 1. Find time at which the case vs date becomes linear in each plot
2. Fit linear model for each city
# What is the predict # of cases for the next few days?
# How is the model performing historically?
Corona_Cases.US_state.summary<-ddply(Corona_Cases.US_state,
c("Province.State","Date"),
summarise,
Total_confirmed_cases_perstate=sum(Total_confirmed_cases)) %>%
filter(Total_confirmed_cases_perstate>100)
# Compute the states with the most cases (for coloring and for linear model)
top_states_totals<-head(ddply(Corona_Cases.US_state.summary,c("Province.State"),summarise, Total_confirmed_cases_perstate.max=max(Total_confirmed_cases_perstate)) %>% arrange(-Total_confirmed_cases_perstate.max),n=max_colors)
kable(top_states_totals,caption = "Top 12 States, total count ")
top_states<-top_states_totals$Province.State
# Manually fix states so that Maryland is switched out for New York
top_states_modified<-c(top_states[top_states !="New York"],"Maryland")
# Plot with all states:
(Corona_Cases.US_state.summary.plot<-ggplot(Corona_Cases.US_state.summary,aes(x=Date,y=Total_confirmed_cases_perstate))+
geom_point()+
geom_point(data=filter(Corona_Cases.US_state.summary,Province.State %in% top_states),aes(col=Province.State))+
scale_color_brewer(type = "qualitative",palette = "Paired")+
default_theme+
theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
ggtitle("Total confirmed cases per state, top 12 colored")+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))
##------------------------------------------
## Fit linear model to time vs total cases
##-----------------------------------------
# First, find the date at which each state's cases vs time becomes lienar (2nd derivative is about 0)
li<-ddply(Corona_Cases.US_state.summary,c("Province.State"),find_linear_index)
# Compute linear model for each state starting at the point at which data becomes linear
for(i in 1:nrow(li)){
Province.State.i<-li[i,"Province.State"]
date.i<-li[i,"V1"]
data.i<-filter(Corona_Cases.US_state.summary,Province.State==Province.State.i & as.numeric(Date) >= date.i)
model_results<-lm(data.i,formula = Total_confirmed_cases_perstate~Date)
slope<-model_results$coefficients[2]
intercept<-model_results$coefficients[1]
li[li$Province.State==Province.State.i,"m"]<-slope
li[li$Province.State==Province.State.i,"b"]<-intercept
}
# Compute top state case load with fitted model
(Corona_Cases.US_state.lm.plot<-ggplot(filter(Corona_Cases.US_state.summary,Province.State %in% top_states_modified ))+
geom_abline(data=filter(li,Province.State %in% top_states_modified),
aes(slope = m,intercept = b,col=Province.State),lty=2)+
geom_point(aes(x=Date,y=Total_confirmed_cases_perstate,col=Province.State))+
scale_color_brewer(type = "qualitative",palette = "Paired")+
default_theme+
theme(axis.text.x = element_text(angle=45,hjust=1),legend.position = "bottom")+
ggtitle("Total confirmed cases per state, top 12 colored")+
scale_x_date(date_breaks="1 week",date_minor_breaks="1 day"))
##------------------------------------------
## Predict the number of total cases over the next week
##-----------------------------------------
predicted_days<-c(0,1,2,3,7)+as.numeric(as.Date("2020-04-20"))
predicted_days_df<-data.frame(matrix(ncol=3))
names(predicted_days_df)<-c("Province.State","days","Total_confirmed_cases_perstate")
# USe model parameters to estiamte case loads
for(state.i in top_states_modified){
predicted_days_df<-rbind(predicted_days_df,
data.frame(Province.State=state.i,
prediction_model(m = li[li$Province.State==state.i,"m"],
b =li[li$Province.State==state.i,"b"] ,
days =predicted_days )))
}
predicted_days_df$Date<-as.Date(predicted_days_df$days,origin="1970-01-01")
kable(predicted_days_df,caption = "Predicted total cases over the next week for selected states")
##------------------------------------------
## Write plots
##-----------------------------------------
write_plot(Corona_Cases.US_state.summary.plot,wd = results_dir)
write_plot(Corona_Cases.US_state.lm.plot,wd = results_dir)
##------------------------------------------
## Write tables
##-----------------------------------------
write.csv(predicted_days_df,file = paste0(results_dir,"predicted_total_cases_days.csv"),quote = F,row.names = F)
Q2: What is the predicted number of cases?
What is the prediction of COVID-19 based on model thus far? Additional questions:
WHy did it take to day 40 to start a log linear trend? How long will it be till x number of cases? When will the plateu happen? Are any effects noticed with social distancing? Delays
##------------------------------------------
## Prediction and Prediction Accuracy
##------------------------------------------
today_num<-max(Corona_Cases.US$Days_since_100)
predicted_days<-today_num+c(1,2,3,7)
#mods = dlply(mydf, .(x3), lm, formula = y ~ x1 + x2)
#today:
Corona_Cases.US[Corona_Cases.US$Days_since_100==(today_num-1),]
Corona_Cases.US[Corona_Cases.US$Days_since_100==today_num,]
Corona_Cases.US$type<-"Historical"
#prediction_values<-prediction_model(m=slope,b=intercept,days = predicted_days)$Total_confirmed_cases
histoical_model<-data.frame(date=today_num,m=slope,b=intercept)
tmp<-data.frame(state=rep(c("A","B"),each=3),x=c(1,2,3,4,5,6))
tmp$y<-c(tmp[1:3,"x"]+5,tmp[4:6,"x"]*5+1)
ddply(tmp,c("state"))
lm(data =tmp,formula = y~x )
train_lm<-function(input_data,subset_coulmn,formula_input){
case_models <- dlply(input_data, subset_coulmn, lm, formula = formula_input)
case_models.parameters <- ldply(case_models, coef)
case_models.parameters<-rename(case_models.parameters,c("b"="(Intercept)","m"=subset_coulmn))
return(case_models.parameters)
}
train_lm(tmp,"state")
dlply(input_data, subset_coulmn, lm,m=)
# model for previous y days
#historical_model_predictions<-data.frame(day_x=NULL,Days_since_100=NULL,Total_confirmed_cases=NULL,Total_confirmed_cases.log=NULL)
# for(i in c(1,2,3,4,5,6,7,8,9,10)){
# #i<-1
# day_x<-today_num-i # 1, 2, 3, 4
# day_x_nextweek<-day_x+c(1,2,3)
# model_fit_x<-lm(data = filter(Corona_Cases.US.case100,Days_since_100 < day_x),formula = Total_confirmed_cases.log~Days_since_100)
# prediction_day_x_nextweek<-prediction_model(m = model_fit_x$coefficients[2],b = model_fit_x$coefficients[1],days = day_x_nextweek)
# prediction_day_x_nextweek$type<-"Predicted"
# acutal_day_x_nextweek<-filter(Corona_Cases.US,Days_since_100 %in% day_x_nextweek) %>% select(c(Days_since_100,Total_confirmed_cases,Total_confirmed_cases.log))
# acutal_day_x_nextweek$type<-"Historical"
# historical_model_predictions.i<-data.frame(day_x=day_x,rbind(acutal_day_x_nextweek,prediction_day_x_nextweek))
# historical_model_predictions<-rbind(historical_model_predictions.i,historical_model_predictions)
# }
#historical_model_predictions.withHx<-rbind.fill(historical_model_predictions,data.frame(Corona_Cases.US,type="Historical"))
#historical_model_predictions.withHx$Total_confirmed_cases.log2<-log(historical_model_predictions.withHx$Total_confirmed_cases,2)
(historical_model_predictions.plot<-ggplot(historical_model_predictions.withHx,aes(x=Days_since_100,y=Total_confirmed_cases.log,col=type))+
geom_point(size=3)+
default_theme+
theme(legend.position = "bottom")+
#geom_abline(slope = slope,intercept =intercept,lty=2)+
#facet_wrap(~case_type,ncol=1)+
scale_color_manual(values = c("Historical"="#377eb8","Predicted"="#e41a1c")))
write_plot(historical_model_predictions.plot,wd=results_dir)
Q3: What is the effect on social distancing, descreased mobility on case load?
Load data from Google which compoutes % change in user mobility relative to baseline for * Recreation
* Workplace
* Residence
* Park
* Grocery
Data from https://www.google.com/covid19/mobility/
# See pre-processing section for script on gathering mobility data
# UNDER DEVELOPMENT
mobility<-read.csv("/Users/stevensmith/Projects/MIT_COVID19/mobility.csv",header = T,stringsAsFactors = F)
#mobility$Retail_Recreation<-as.numeric(sub(mobility$Retail_Recreation,pattern = "%",replacement = ""))
#mobility$Workplace<-as.numeric(sub(mobility$Workplace,pattern = "%",replacement = ""))
#mobility$Residential<-as.numeric(sub(mobility$Residential,pattern = "%",replacement = ""))
##------------------------------------------
## Show relationship between mobility and caseload
##------------------------------------------
mobility$County<-gsub(mobility$County,pattern = " County",replacement = "")
Corona_Cases.US_state.mobility<-merge(Corona_Cases.US_state,plyr::rename(mobility,c("State"="Province.State","County"="City")))
#Corona_Cases.US_state.tmp<-merge(metadata,Corona_Cases.US_state.tmp)
# Needs to happen upsteam, see todos
#Corona_Cases.US_state.tmp$Total_confirmed_cases.perperson<-Corona_Cases.US_state.tmp$Total_confirmed_cases/as.numeric(Corona_Cases.US_state.tmp$Population)
mobility_measures<-c("Retail_Recreation","Grocery_Pharmacy","Parks","Transit","Workplace","Residential")
plot_data<-filter(Corona_Cases.US_state.mobility, Date.numeric==max(Corona_Cases.US_state$Date.numeric) ) %>% melt(measure.vars=mobility_measures)
plot_data$value<-as.numeric(gsub(plot_data$value,pattern = "%",replacement = ""))
plot_data<-filter(plot_data,!is.na(value))
(mobility.plot<-ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
facet_grid(Province.State~variable,scales = "free")+
xlab("Mobility change from baseline (%)")+
ylab(paste0("Confirmed cases per 100 people(Today)"))+
default_theme+
ggtitle("Mobility change vs cases"))

(mobility.global.plot<-ggplot(plot_data,aes(y=Total_confirmed_cases.per100,x=value))+geom_point()+
facet_wrap(~variable,scales = "free")+
xlab("Mobility change from baseline (%)")+
ylab(paste0("Confirmed cases (Today) per 100 people"))+
default_theme+
ggtitle("Mobility change vs cases"))

plot_data.permobility_summary<-ddply(plot_data,c("Province.State","variable"),summarise,cor=cor(y =Total_confirmed_cases.per100,x=value),median_change=median(x=value)) %>% arrange(-abs(cor))
kable(plot_data.permobility_summary,caption = "Ranked per-state mobility correlation with total confirmed cases")
Ranked per-state mobility correlation with total confirmed cases
| Alaska |
Transit |
-1.0000000 |
-63.0 |
| Delaware |
Retail_Recreation |
1.0000000 |
-39.5 |
| Delaware |
Grocery_Pharmacy |
1.0000000 |
-17.5 |
| Delaware |
Parks |
-1.0000000 |
20.5 |
| Delaware |
Transit |
1.0000000 |
-37.0 |
| Delaware |
Workplace |
1.0000000 |
-37.0 |
| Delaware |
Residential |
-1.0000000 |
14.0 |
| Hawaii |
Retail_Recreation |
0.9931972 |
-56.0 |
| Hawaii |
Grocery_Pharmacy |
0.9695437 |
-34.0 |
| New Hampshire |
Parks |
0.9582784 |
-20.0 |
| Connecticut |
Grocery_Pharmacy |
-0.9087695 |
-6.0 |
| Maine |
Transit |
-0.9040896 |
-50.0 |
| Alaska |
Residential |
0.8872480 |
13.0 |
| Utah |
Residential |
-0.8675952 |
12.0 |
| South Dakota |
Parks |
0.8656364 |
-26.0 |
| Vermont |
Parks |
0.8542006 |
-35.5 |
| Alaska |
Grocery_Pharmacy |
-0.8062819 |
-7.0 |
| Hawaii |
Residential |
-0.7854909 |
19.0 |
| Utah |
Transit |
-0.7846772 |
-18.0 |
| Massachusetts |
Workplace |
-0.7625388 |
-39.0 |
| Connecticut |
Transit |
-0.7616657 |
-50.0 |
| Rhode Island |
Workplace |
-0.7503039 |
-39.5 |
| Wyoming |
Parks |
-0.7347997 |
-4.0 |
| Alaska |
Workplace |
-0.7314780 |
-34.0 |
| Wyoming |
Transit |
-0.7208980 |
-17.0 |
| Utah |
Parks |
-0.6853389 |
17.0 |
| Hawaii |
Parks |
0.6813458 |
-72.0 |
| Vermont |
Grocery_Pharmacy |
-0.6540895 |
-25.0 |
| New York |
Workplace |
-0.6469077 |
-34.5 |
| Utah |
Workplace |
-0.6448624 |
-37.0 |
| Maine |
Workplace |
-0.6433751 |
-30.0 |
| Arizona |
Grocery_Pharmacy |
-0.6397647 |
-15.0 |
| Rhode Island |
Retail_Recreation |
-0.6273853 |
-45.0 |
| Montana |
Workplace |
-0.6239388 |
-40.5 |
| Hawaii |
Transit |
0.6188732 |
-89.0 |
| Rhode Island |
Residential |
-0.6164663 |
18.5 |
| Nebraska |
Workplace |
0.6064648 |
-32.5 |
| New Jersey |
Workplace |
-0.6030508 |
-44.0 |
| New Jersey |
Parks |
-0.5973055 |
-6.0 |
| New York |
Retail_Recreation |
-0.5872062 |
-46.0 |
| Connecticut |
Residential |
0.5408283 |
14.0 |
| Hawaii |
Workplace |
0.5396454 |
-46.0 |
| North Dakota |
Retail_Recreation |
-0.5366923 |
-42.0 |
| New York |
Parks |
0.5276211 |
20.0 |
| Connecticut |
Retail_Recreation |
-0.5187090 |
-45.0 |
| Massachusetts |
Retail_Recreation |
-0.5173605 |
-44.0 |
| North Dakota |
Parks |
0.5131966 |
-34.0 |
| Arizona |
Retail_Recreation |
-0.5094635 |
-42.5 |
| Maine |
Parks |
0.5035091 |
-31.0 |
| Connecticut |
Workplace |
-0.5023981 |
-39.0 |
| New Jersey |
Retail_Recreation |
-0.5022912 |
-62.5 |
| Montana |
Parks |
-0.4913929 |
-58.0 |
| Wyoming |
Workplace |
-0.4876002 |
-31.0 |
| Nebraska |
Residential |
-0.4855502 |
14.0 |
| New Jersey |
Grocery_Pharmacy |
-0.4841000 |
2.5 |
| New Mexico |
Grocery_Pharmacy |
-0.4729771 |
-11.0 |
| Rhode Island |
Parks |
0.4729613 |
52.0 |
| Montana |
Residential |
0.4701424 |
14.0 |
| Iowa |
Parks |
-0.4668678 |
28.5 |
| New Mexico |
Parks |
0.4492647 |
-31.5 |
| Illinois |
Transit |
-0.4485191 |
-31.0 |
| New Mexico |
Residential |
0.4475620 |
13.5 |
| Kansas |
Parks |
0.4429949 |
72.0 |
| Pennsylvania |
Workplace |
-0.4334272 |
-36.0 |
| Vermont |
Residential |
0.4326972 |
11.5 |
| South Carolina |
Workplace |
0.4308558 |
-30.0 |
| New Jersey |
Transit |
-0.4281583 |
-50.5 |
| Idaho |
Workplace |
-0.4272893 |
-29.0 |
| Arizona |
Residential |
0.4242786 |
13.0 |
| Kentucky |
Parks |
-0.4210859 |
28.5 |
| California |
Transit |
-0.4205470 |
-42.0 |
| Wisconsin |
Transit |
-0.4194766 |
-23.5 |
| Massachusetts |
Grocery_Pharmacy |
-0.4187413 |
-7.0 |
| New Hampshire |
Residential |
-0.4173083 |
14.0 |
| Montana |
Retail_Recreation |
-0.4145442 |
-51.0 |
| California |
Residential |
0.4100448 |
14.0 |
| Idaho |
Grocery_Pharmacy |
-0.4028251 |
-4.5 |
| Idaho |
Transit |
-0.3997869 |
-30.0 |
| Maryland |
Workplace |
-0.3966089 |
-35.0 |
| Maryland |
Grocery_Pharmacy |
-0.3959632 |
-10.0 |
| Alabama |
Grocery_Pharmacy |
-0.3951991 |
-2.0 |
| Nevada |
Transit |
-0.3925209 |
-20.0 |
| Montana |
Transit |
-0.3892824 |
-41.0 |
| Arizona |
Transit |
0.3811607 |
-38.0 |
| Alabama |
Workplace |
-0.3804983 |
-29.0 |
| New York |
Transit |
-0.3734401 |
-48.0 |
| Wyoming |
Grocery_Pharmacy |
-0.3665922 |
-10.0 |
| West Virginia |
Parks |
0.3664071 |
-33.0 |
| Pennsylvania |
Retail_Recreation |
-0.3563090 |
-45.0 |
| New Mexico |
Retail_Recreation |
-0.3498975 |
-42.5 |
| Michigan |
Parks |
0.3415369 |
30.0 |
| Nebraska |
Grocery_Pharmacy |
0.3391776 |
-0.5 |
| Florida |
Residential |
0.3316478 |
14.0 |
| Pennsylvania |
Parks |
0.3292591 |
13.0 |
| California |
Parks |
-0.3289171 |
-38.5 |
| Montana |
Grocery_Pharmacy |
-0.3279939 |
-16.0 |
| Alabama |
Transit |
-0.3259993 |
-36.5 |
| Alaska |
Retail_Recreation |
0.3245901 |
-39.0 |
| North Dakota |
Workplace |
0.3153823 |
-40.0 |
| Minnesota |
Transit |
-0.3117365 |
-28.5 |
| North Carolina |
Grocery_Pharmacy |
0.3108803 |
0.0 |
| Maine |
Retail_Recreation |
-0.3088429 |
-42.0 |
| Arkansas |
Parks |
-0.3039396 |
-12.0 |
| West Virginia |
Grocery_Pharmacy |
-0.3036763 |
-6.0 |
| Vermont |
Retail_Recreation |
0.3014240 |
-57.0 |
| Idaho |
Retail_Recreation |
-0.2950909 |
-40.5 |
| Colorado |
Residential |
0.2881398 |
14.0 |
| Mississippi |
Residential |
0.2863912 |
13.0 |
| Maryland |
Retail_Recreation |
-0.2832871 |
-39.0 |
| Virginia |
Transit |
-0.2818214 |
-33.0 |
| Arkansas |
Retail_Recreation |
-0.2789239 |
-30.0 |
| Texas |
Residential |
-0.2763167 |
15.0 |
| Rhode Island |
Transit |
-0.2749771 |
-56.0 |
| Kansas |
Workplace |
0.2727601 |
-32.5 |
| North Carolina |
Workplace |
0.2719939 |
-31.0 |
| Vermont |
Workplace |
-0.2673684 |
-43.0 |
| Nevada |
Residential |
0.2631416 |
17.0 |
| Maryland |
Residential |
0.2626017 |
15.0 |
| Utah |
Retail_Recreation |
-0.2610567 |
-40.0 |
| Oregon |
Grocery_Pharmacy |
0.2604544 |
-7.0 |
| Rhode Island |
Grocery_Pharmacy |
0.2590564 |
-7.5 |
| Nevada |
Retail_Recreation |
-0.2584506 |
-43.0 |
| Texas |
Workplace |
0.2580831 |
-32.0 |
| Illinois |
Workplace |
-0.2516389 |
-31.0 |
| Tennessee |
Workplace |
-0.2508101 |
-31.0 |
| Texas |
Parks |
0.2503155 |
-42.0 |
| Florida |
Parks |
-0.2489220 |
-43.0 |
| Wisconsin |
Parks |
0.2478975 |
51.5 |
| Tennessee |
Residential |
0.2465179 |
11.5 |
| Illinois |
Parks |
0.2464331 |
26.5 |
| South Carolina |
Parks |
-0.2421267 |
-23.0 |
| California |
Grocery_Pharmacy |
-0.2413883 |
-11.5 |
| Pennsylvania |
Grocery_Pharmacy |
-0.2411169 |
-6.0 |
| California |
Retail_Recreation |
-0.2364956 |
-44.0 |
| Georgia |
Grocery_Pharmacy |
-0.2333103 |
-10.0 |
| Missouri |
Residential |
-0.2315271 |
13.0 |
| Arkansas |
Residential |
0.2314189 |
12.0 |
| New York |
Grocery_Pharmacy |
-0.2313466 |
8.0 |
| Washington |
Workplace |
-0.2231765 |
-38.0 |
| Idaho |
Residential |
-0.2139764 |
11.0 |
| North Carolina |
Transit |
0.2127722 |
-32.0 |
| Michigan |
Workplace |
-0.2111759 |
-40.0 |
| California |
Workplace |
-0.2094557 |
-36.0 |
| New Jersey |
Residential |
0.2076436 |
18.0 |
| North Carolina |
Residential |
0.2015529 |
13.0 |
| Kansas |
Grocery_Pharmacy |
-0.1999844 |
-14.0 |
| Mississippi |
Grocery_Pharmacy |
-0.1974360 |
-8.0 |
| Iowa |
Transit |
0.1944439 |
-24.0 |
| Illinois |
Residential |
0.1931903 |
14.0 |
| Oregon |
Residential |
0.1913519 |
10.5 |
| Georgia |
Workplace |
-0.1892298 |
-33.5 |
| North Dakota |
Grocery_Pharmacy |
-0.1883518 |
-8.0 |
| Missouri |
Workplace |
0.1883338 |
-28.5 |
| Colorado |
Parks |
-0.1789064 |
2.0 |
| South Dakota |
Transit |
-0.1747711 |
-40.0 |
| New Mexico |
Transit |
0.1725590 |
-38.5 |
| Georgia |
Retail_Recreation |
-0.1716591 |
-41.0 |
| Virginia |
Residential |
0.1710728 |
14.0 |
| Wisconsin |
Residential |
-0.1676647 |
14.0 |
| Connecticut |
Parks |
0.1654319 |
43.0 |
| Florida |
Retail_Recreation |
0.1624300 |
-43.0 |
| Ohio |
Transit |
0.1618303 |
-28.0 |
| Virginia |
Grocery_Pharmacy |
-0.1589315 |
-8.0 |
| Washington |
Residential |
0.1548912 |
13.0 |
| Minnesota |
Parks |
0.1539478 |
-9.0 |
| South Carolina |
Residential |
-0.1533238 |
12.0 |
| Virginia |
Parks |
0.1516374 |
6.0 |
| Georgia |
Residential |
-0.1508705 |
13.0 |
| Oklahoma |
Residential |
0.1500992 |
15.0 |
| Indiana |
Retail_Recreation |
0.1472029 |
-38.0 |
| New Hampshire |
Retail_Recreation |
-0.1426800 |
-41.0 |
| Alabama |
Parks |
0.1420917 |
-1.0 |
| South Dakota |
Retail_Recreation |
-0.1418970 |
-38.5 |
| Massachusetts |
Parks |
0.1406265 |
39.0 |
| Indiana |
Residential |
0.1395374 |
12.0 |
| Mississippi |
Transit |
-0.1378276 |
-38.5 |
| Michigan |
Retail_Recreation |
-0.1372488 |
-53.0 |
| North Dakota |
Transit |
0.1366526 |
-48.0 |
| Massachusetts |
Transit |
-0.1343047 |
-45.0 |
| Alabama |
Retail_Recreation |
0.1321146 |
-39.0 |
| Washington |
Grocery_Pharmacy |
0.1320256 |
-7.0 |
| South Dakota |
Residential |
0.1317904 |
15.0 |
| Oregon |
Retail_Recreation |
0.1314695 |
-41.0 |
| Pennsylvania |
Transit |
-0.1300246 |
-41.5 |
| Ohio |
Parks |
-0.1286516 |
67.5 |
| Maine |
Residential |
-0.1273953 |
11.0 |
| Washington |
Transit |
-0.1267337 |
-33.5 |
| North Carolina |
Parks |
-0.1189864 |
7.0 |
| Texas |
Transit |
0.1189128 |
-41.0 |
| Wyoming |
Retail_Recreation |
-0.1182525 |
-39.0 |
| Oregon |
Parks |
0.1158120 |
16.5 |
| Oklahoma |
Parks |
-0.1142092 |
-18.5 |
| Kansas |
Transit |
-0.1139995 |
-26.5 |
| Kentucky |
Grocery_Pharmacy |
0.1135633 |
4.0 |
| Florida |
Workplace |
-0.1121572 |
-33.0 |
| New Hampshire |
Grocery_Pharmacy |
-0.1121380 |
-6.0 |
| Massachusetts |
Residential |
0.1119788 |
15.0 |
| Mississippi |
Retail_Recreation |
-0.1109884 |
-40.0 |
| Wisconsin |
Workplace |
-0.1104923 |
-31.0 |
| Minnesota |
Workplace |
-0.1097801 |
-33.0 |
| Maryland |
Transit |
-0.1097332 |
-39.0 |
| Texas |
Grocery_Pharmacy |
0.1089536 |
-14.0 |
| Idaho |
Parks |
0.1083222 |
-22.0 |
| Arkansas |
Workplace |
-0.1078073 |
-26.0 |
| Wyoming |
Residential |
0.1061846 |
12.5 |
| Arkansas |
Transit |
0.1053288 |
-27.0 |
| Arizona |
Workplace |
-0.1047115 |
-35.0 |
| Ohio |
Residential |
0.1043167 |
14.0 |
| Nebraska |
Retail_Recreation |
0.1042186 |
-36.0 |
| Minnesota |
Retail_Recreation |
0.1026423 |
-40.0 |
| Maine |
Grocery_Pharmacy |
-0.1025823 |
-13.0 |
| Indiana |
Parks |
-0.1016907 |
29.0 |
| South Dakota |
Workplace |
0.1014981 |
-35.0 |
| Mississippi |
Workplace |
-0.1004460 |
-33.0 |
| Wisconsin |
Grocery_Pharmacy |
0.0991707 |
-1.0 |
| Georgia |
Parks |
0.0925718 |
-6.0 |
| New York |
Residential |
0.0920025 |
17.5 |
| Oklahoma |
Grocery_Pharmacy |
-0.0911595 |
-1.0 |
| New Hampshire |
Transit |
-0.0903033 |
-57.0 |
| Virginia |
Workplace |
-0.0902029 |
-31.5 |
| Indiana |
Workplace |
0.0896554 |
-34.0 |
| Pennsylvania |
Residential |
0.0875187 |
15.0 |
| West Virginia |
Residential |
-0.0871474 |
11.0 |
| Missouri |
Transit |
-0.0862899 |
-24.5 |
| Kentucky |
Transit |
0.0825242 |
-31.0 |
| Virginia |
Retail_Recreation |
-0.0815464 |
-35.0 |
| South Carolina |
Transit |
0.0808945 |
-45.0 |
| Nevada |
Parks |
0.0804126 |
-12.5 |
| Michigan |
Grocery_Pharmacy |
-0.0798123 |
-11.0 |
| Nebraska |
Transit |
-0.0771981 |
-9.0 |
| Kentucky |
Retail_Recreation |
0.0764110 |
-29.0 |
| Indiana |
Grocery_Pharmacy |
-0.0757445 |
-5.5 |
| South Carolina |
Retail_Recreation |
-0.0746272 |
-35.0 |
| Michigan |
Residential |
0.0745420 |
15.0 |
| Colorado |
Transit |
0.0738397 |
-36.0 |
| Tennessee |
Parks |
-0.0734331 |
10.5 |
| Washington |
Parks |
0.0713444 |
-3.5 |
| Ohio |
Grocery_Pharmacy |
0.0710696 |
0.0 |
| Michigan |
Transit |
0.0686961 |
-46.0 |
| North Dakota |
Residential |
-0.0651724 |
17.0 |
| Oregon |
Workplace |
-0.0646101 |
-31.0 |
| Minnesota |
Grocery_Pharmacy |
0.0642601 |
-6.0 |
| Kentucky |
Residential |
0.0624202 |
12.0 |
| North Carolina |
Retail_Recreation |
0.0620898 |
-34.0 |
| Ohio |
Retail_Recreation |
0.0605859 |
-36.0 |
| West Virginia |
Workplace |
0.0602897 |
-33.0 |
| Nebraska |
Parks |
0.0595751 |
55.5 |
| Oklahoma |
Workplace |
0.0585898 |
-31.0 |
| West Virginia |
Retail_Recreation |
-0.0573394 |
-38.5 |
| South Dakota |
Grocery_Pharmacy |
0.0548553 |
-9.0 |
| Washington |
Retail_Recreation |
-0.0531770 |
-42.0 |
| South Carolina |
Grocery_Pharmacy |
0.0527702 |
1.0 |
| Iowa |
Retail_Recreation |
-0.0514844 |
-38.0 |
| New Hampshire |
Workplace |
0.0512132 |
-37.0 |
| Oregon |
Transit |
0.0500688 |
-27.5 |
| Florida |
Grocery_Pharmacy |
0.0486905 |
-14.0 |
| Missouri |
Parks |
0.0483760 |
0.0 |
| Missouri |
Retail_Recreation |
-0.0476974 |
-36.0 |
| Arizona |
Parks |
-0.0475900 |
-44.5 |
| Kentucky |
Workplace |
-0.0472408 |
-36.0 |
| Missouri |
Grocery_Pharmacy |
0.0471788 |
2.0 |
| Illinois |
Grocery_Pharmacy |
-0.0413057 |
2.0 |
| Indiana |
Transit |
0.0403144 |
-29.0 |
| Illinois |
Retail_Recreation |
0.0401067 |
-40.0 |
| West Virginia |
Transit |
-0.0400159 |
-45.0 |
| Texas |
Retail_Recreation |
0.0380055 |
-40.0 |
| Florida |
Transit |
-0.0376599 |
-49.0 |
| Colorado |
Grocery_Pharmacy |
-0.0359283 |
-17.0 |
| Nevada |
Workplace |
0.0327453 |
-40.0 |
| Colorado |
Retail_Recreation |
-0.0317055 |
-44.0 |
| Minnesota |
Residential |
-0.0314567 |
17.0 |
| Ohio |
Workplace |
-0.0294480 |
-35.0 |
| Utah |
Grocery_Pharmacy |
0.0286012 |
-4.0 |
| Mississippi |
Parks |
-0.0280012 |
-25.0 |
| Tennessee |
Grocery_Pharmacy |
0.0252797 |
6.0 |
| Oklahoma |
Retail_Recreation |
0.0251635 |
-31.0 |
| Tennessee |
Transit |
-0.0212160 |
-32.0 |
| Iowa |
Workplace |
-0.0205845 |
-30.0 |
| Alabama |
Residential |
-0.0198444 |
11.0 |
| Wisconsin |
Retail_Recreation |
0.0185339 |
-44.0 |
| New Mexico |
Workplace |
0.0181485 |
-34.0 |
| Kansas |
Residential |
-0.0175172 |
13.0 |
| Iowa |
Residential |
-0.0141633 |
13.0 |
| Kansas |
Retail_Recreation |
-0.0132294 |
-37.0 |
| Georgia |
Transit |
-0.0129488 |
-35.0 |
| Maryland |
Parks |
-0.0075696 |
27.0 |
| Vermont |
Transit |
0.0068563 |
-63.0 |
| Oklahoma |
Transit |
0.0065825 |
-26.0 |
| Nevada |
Grocery_Pharmacy |
0.0061812 |
-12.5 |
| Colorado |
Workplace |
0.0054031 |
-39.0 |
| Tennessee |
Retail_Recreation |
-0.0049494 |
-30.0 |
| Iowa |
Grocery_Pharmacy |
0.0024907 |
4.0 |
| Arkansas |
Grocery_Pharmacy |
0.0014307 |
3.0 |
| Alaska |
Parks |
NA |
29.0 |
| District of Columbia |
Retail_Recreation |
NA |
-69.0 |
| District of Columbia |
Grocery_Pharmacy |
NA |
-28.0 |
| District of Columbia |
Parks |
NA |
-65.0 |
| District of Columbia |
Transit |
NA |
-69.0 |
| District of Columbia |
Workplace |
NA |
-48.0 |
| District of Columbia |
Residential |
NA |
17.0 |
# sanity check
ggplot(filter(plot_data,Province.State %in% c("Pennsylvania","Maryland","New Jersey","California","Delaware","Connecticut")),aes(x=Total_confirmed_cases.per100,fill=variable))+geom_histogram()+
facet_grid(~Province.State)+
default_theme+
theme(legend.position = "bottom")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

write_plot(mobility.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.plot.png"
write_plot(mobility.global.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/mobility.global.plot.png"
(plot_data.permobility_summary.plot<-ggplot(plot_data.permobility_summary,aes(x=variable,y=median_change))+
geom_jitter(size=2,width=.2)+
#geom_jitter(data=plot_data.permobility_summary %>% arrange(-abs(median_change)) %>% head(n=15),aes(col=Province.State),size=2,width=.2)+
default_theme+
ggtitle("Per-Sate Median Change in Mobility")+
xlab("Mobility Meaure")+
ylab("Median Change from Baseline"))

write_plot(plot_data.permobility_summary.plot,wd = results_dir)
## [1] "/Users/stevensmith/Projects/coronavirus/results/plot_data.permobility_summary.plot.png"